<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Streaming on Sergio A. Mora Pardo | AI/ML Technical Lead</title><link>https://sergiomorapardo.github.io/tags/streaming/</link><description>Recent content in Streaming on Sergio A. Mora Pardo | AI/ML Technical Lead</description><generator>Hugo</generator><language>en-US</language><atom:link href="https://sergiomorapardo.github.io/tags/streaming/index.xml" rel="self" type="application/rss+xml"/><item><title>Production ML Serving Architecture</title><link>https://sergiomorapardo.github.io/work/production-ml-serving/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sergiomorapardo.github.io/work/production-ml-serving/</guid><description>&lt;h2 id="the-problem"&gt;The problem&lt;/h2&gt;&#10;&lt;p&gt;Personalized messaging required fresh behavioral context and low-latency decisions, but many useful ML features and scores were most reliable and economical to compute in batch. The serving design needed to connect both worlds without making campaign delivery depend on slow analytical queries.&lt;/p&gt;&#10;&lt;h2 id="my-role"&gt;My role&lt;/h2&gt;&#10;&lt;p&gt;I designed the production serving architecture and its decision flow, aligning model computation, storage, APIs, session events, and product rules around the latency and reliability requirements of push and email delivery.&lt;/p&gt;</description></item></channel></rss>